Stochastic Process
This page contains resources about Stochastic Processes, Stochastic Systems, Random Processes and Random Fields. More specific information is included in each subfield. Subfields and Concepts See Category:Stochastic Processes for some of its subfields. * Discrete-time Stochastic Processes * Continuous-time Stochastic Processes * State Space ** Discrete ** Continuous * Weak-sense Stationary Process / Wide-sense Stationary Process * Strictly Stationary Process / Strongly Stationary Process * Ergodicity / Ergodic Process ** Mean-ergodic ** Autocovariance-ergodic ** Ergodic in the wide sense * Stochastic Calculus * Time domain ** Autocorrelation Function * Frequency domain ** Power Spectral Density / Power spectrum / Spectrum ** Cramér Spectral Representation * Wold Decomposition Theorem / Wold Representation Theorem * Time Series Processes (Discrete-time and Continuous State Space) ** Autoregressive (AR) Process ** Moving Average (MA) Process ** ARMA Process * Latent Variable Models (i.e. Partially Observed Probabilistic Models) ** Continuous Latent Variable Models ** Discrete Latent Variable Models * Stochastic Dynamical System * Random Dynamical System * Random Graphs * Random Fields ** Markov Random Field ** Gibbs Random Field ** Gaussian Random Field * Markov Models ** Discrete-time Markov Chain (Discrete-time and Discrete State Space) ** Discrete-time Harris Chain (Discrete-time and Continuous State Space) ** Continuous-time Markov Chain / Continuous-time Markov Process / Markov Jump Process ** Continuous-time Stochastic Process with the Markov property (e.g. Wiener Process) ** Hidden Markov Model ** Markov Decision Process ** Partially Observable Markov Decision Process ** Hierarchical Markov Models * Gaussian Process * Gauss–Markov Process / AR Process * Ornstein-Uhlenbeck Process / Stationary Gauss–Markov Process * Wiener Process (Continuous-time and Continuous State Space) * Geometric Brownian Motion * Harmonic Process (e.g. Sinusoidal Model) * Innovations Process * Queues * Martingales * Jump Process * Point Process ** Cox Point Process ** Poisson Process * Dirichlet Process * Pitman–Yor Process * Chinese Restaurant Process * Indian Buffet Process * Lévy Process * Bernoulli Process * Pólya's Urn Process * Hoppe's Urn Process * Stick Breaking Process Online Courses Video Lectures * Discrete Stochastic Processes by Robert Gallager Lecture Notes * Stochastic Processes by Cosma Shalizi * Introduction to Stochastic Systems by Maxim Raginsky * Introduction to Stochastic Processes by Hao Wu * Advanced Stochastic Processes by David Gamarnik * Stochastic Systems by Florian Herzog Books and Book Chapters See Amazon and Google-Books for more books. * Hajek, B. (2015). Random Processes for Engineers. Cambridge University Press. * Pavliotis, G. A. (2014). Stochastic Processes and Applications. Springer. * Stark, H., Woods, J. W., Thilaka, B., & Kumar, A. (2012). Probability, statistics, and random processes for engineers. 4th Ed. Pearson. * Klebaner, F. C. (2012). Introduction to stochastic calculus with applications. 3rd Ed. Imperial College Press. * Papoulis, A., & Pillai, S. U. (2002). Probability, random variables, and stochastic processes. 4th Ed. Tata McGraw-Hill Education. * Gray, R. M. (2009). Probability, random processes, and ergodic properties. Springer Science & Business Media. * Stirzaker, D. (2005). Stochastic processes and models. Oxford University Press. * Grimmett, G., & Stirzaker, D. (2001). Probability and random processes. 3rd Ed. Oxford University Press. * Kao, E. P. (1997). An introduction to stochastic processes. Duxbury. * Ross, S. M. (1996). Stochastic processes. 2nd Ed. John Wiley & Sons. * Stark, H., & Woods, J. W. (1994). Probability, random processes, and estimation theory for engineers. Prentice Hall. * Helstrom, C.W., (1992). Probability and Stochastic Processes for Engineers. 2nd Ed. Addison-Wesley. * Bartlett, M. S. (1978). An Introduction to Stochastic Processes, with Special Reference to Methods and Applications. Cambridge University Press. * Doob, J. L. (1953).Stochastic Processes. Wiley. Scholarly Articles * Geering, H. P., Dondi, G., Herzog, F., & Keel, S. (2011). Stochastic systems. Course script. (link) Software * See also * State Space Models / Linear Dynamical Systems * Kalman filter * Information Theory * Probability Theory * Estimation Theory / System Identification / Statistical Signal Processing * Bayesian Nonparametrics * Monte Carlo Methods * Computational Finance Other Resources * Stochastic Processes - Notebook * Random Fields - Notebook * Time Series - Notebook Category:Stochastic Processes Category:Probability and Statistics Category:Probabilistic Graphical Models